sentences, sentence_length = pad_sentences(
conversations,
max_sentence_length=max_sent_len,
max_conversation_length=max_conv_len)
print('Saving preprocessed data at', split_data_dir)
to_pickle(conversation_length, split_data_dir.joinpath('conversation_length.pkl'))
to_pickle(sentences, split_data_dir.joinpath('sentences.pkl'))
to_pickle(sentence_length, split_data_dir.joinpath('sentence_length.pkl'))
to_pickle(emotions, split_data_dir.joinpath('labels.pkl'))
if split_type == 'train':
print('Save Vocabulary...')
vocab = Vocab(tokenizer)
vocab.add_dataframe(conversations)
assert(GLOVE_DIR != "")
vocab.update(GLOVE_DIR, max_size=max_vocab_size, min_freq=min_freq)
print('Vocabulary size: ', len(vocab))
vocab.pickle(dailydialog_dir.joinpath('word2id.pkl'),
dailydialog_dir.joinpath('id2word.pkl'),
dailydialog_dir.joinpath('word_emb.pkl'))
print('Done!')
sentences, sentence_length = pad_sentences(
conv_sentences,
max_sentence_length=max_sent_len,
max_conversation_length=max_conv_len)
for sentence_len, label in zip(conversation_length, conv_labels):
assert(sentence_len ==len(label))
print('Saving preprocessed data at', split_data_dir)
to_pickle(conversation_length, split_data_dir.joinpath(
'conversation_length.pkl'))
to_pickle(sentences, split_data_dir.joinpath('sentences.pkl'))
to_pickle(conv_labels, split_data_dir.joinpath('labels.pkl'))
to_pickle(sentence_length, split_data_dir.joinpath(
'sentence_length.pkl'))
to_pickle(iemocap.vids[split_type], split_data_dir.joinpath('video_id.pkl'))
if split_type == 'train':
print('Save Vocabulary...')
vocab = Vocab(tokenizer)
vocab.add_dataframe(conv_sentences)
assert(GLOVE_DIR != "")
vocab.update(GLOVE_DIR, max_size=max_vocab_size, min_freq=min_freq)
print('Vocabulary size: ', len(vocab))
vocab.pickle(iemocap_dir.joinpath('word2id.pkl'),
iemocap_dir.joinpath('id2word.pkl'),
iemocap_dir.joinpath('word_emb.pkl'))